The new framework of innovation biosphere for analysing innovation policies facing COVID-19 grand challenge.

BACKGROUND: Facing global grand challenges such as coronavirus disease 2019 (COVID-19) require the participation of various actors in different sectors and systematically directing their innovative efforts. Considering the complexity, non-linear dynamics, and global extent of the COVID-19 challenge, developing and applying a multi-level, resilient, and systematic innovative framework is vital. Therefore, this study aims to apply the "innovation biosphere" framework inspired by ecological studies for examining and analysing the management dimensions of COVID-19. METHODS: In this research, based on a deductive-inductive approach, the case study methodology is used. In accordance with this strategy, the innovation biosphere metaphor is considered as the basic framework (deductive approach) and subsequently the grand challenge of COVID-19 (inductive approach) is analysed at three levels: micro, meso and macro. RESULTS: The research findings verify the correspondence between what happened in the management of COVID-19 and the proposed framework of innovation biosphere. In other words, the findings of the research show that the effect of global cooperation, role-playing and co-evolution of different actors and subsystems in facing the grand challenge of COVID-19 under an ecosystemic and eco-innovation approach has been evident. These events subsequently led to the cessation of the pandemic after about four years. CONCLUSIONS: The main policy implications include the role of self-organization, the capability of global value networks, mission orientation, and co-evolution between actors as the contributions of innovation biosphere framework for managing grand health challenges, and global cohesion, oligopoly market, supporting local innovations, the critical role of basic research, and deregulation as the contributions of the COVID-19 case study for enhancing the innovation biosphere metaphor.

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.

While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.

Efficacy of Chemoradiotherapy on Health-Related Quality of Life in Esophageal Cancer Patients with Dysphagia.

INTRODUCTION: Esophageal cancer is diagnosed with more than 480,000 patients per year and this disease became the eighth most common cancer worldwide. AIM: In this study, we tried to investigate the role of chemoradiotherapy in decreasing the severity of dysphagia and increasing the quality of life (QOL) in patients with esophageal cancer. METHODS: Patients were diagnosed with esophageal cancer, which were proven by pathological studies. Also, all of these patients had no primary surgeries for their esophageal cancer. For determining the cancer staging, the endoscopy, sonography, abdominal and pelvic computed tomography scans were assessed. RESULTS: In this study, 81% of patients showed responsiveness to the chemoradiotherapy and their dysphagia significantly was getting improved after treatment in comparison to the initial date (P<0.01). Also, the pain score significantly decreased after chemoradiotherapy. However, the analysis failed to show any significant difference between before and after treatment in 19% of patients who had high degrees of dysphagia and they were the candidate for surgery and stent putting. On the other hand, we demonstrated that there is no correlation between sex, age, tumor type and location with the recovery rate of dysphagia. In addition, we showed that none of the patients showed the recurrence of dysphagia during the study (1.5 years). CONCLUSION: Chemoradiotherapy could be a novel treatment for patients with inoperable esophageal cancer to reduce the severity of dysphagia and increasing the QOL of these individuals.